Download Medical Image Computing and Computer-Assisted Intervention by Sebastien Ourselin, Leo Joskowicz, Mert R. Sabuncu, Gozde PDF

By Sebastien Ourselin, Leo Joskowicz, Mert R. Sabuncu, Gozde Unal, William Wells

The three-volume set LNCS 9900, 9901, and 9902 constitutes the refereed complaints of the nineteenth overseas convention on scientific snapshot Computing and Computer-Assisted Intervention, MICCAI 2016, held in Athens, Greece, in October 2016. in line with rigorous peer studies, this system committee conscientiously chosen 228 revised ordinary papers from 756 submissions for presentation in 3 volumes. The papers were equipped within the following topical sections: half I: mind research, mind research - connectivity; mind research - cortical morphology; Alzheimer illness; surgical information and monitoring; machine aided interventions; ultrasound photo research; melanoma picture research; half II: laptop studying and have choice; deep studying in clinical imaging; functions of laptop studying; segmentation; mobilephone snapshot research; half III: registration and deformation estimation; form modeling; cardiac and vascular photograph research; snapshot reconstruction; and MR picture analysis.

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Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part III

The three-volume set LNCS 9900, 9901, and 9902 constitutes the refereed complaints of the nineteenth foreign convention on clinical picture Computing and Computer-Assisted Intervention, MICCAI 2016, held in Athens, Greece, in October 2016. in response to rigorous peer studies, this system committee conscientiously chosen 228 revised general papers from 756 submissions for presentation in 3 volumes.

Additional info for Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part III

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Simonovsky et al. 7. : Learning similarity measure for multi-modal 3D image registration. In: CVPR, pp. 186–193 (2009) 8. : Fully convolutional networks for semantic segmentation. In: CVPR, pp. 3431–3440 (2015) 9. : Nonrigid multimodality image registration. In: SPIE, vol. 4322, pp. 1609–1620 (2001) 10. : Boosted metric learning for 3D multi-modal deformable registration. In: ISBI, pp. 1209–1214 (2011) 11. : Cross-domain synthesis of medical images using efficient location-sensitive deep network.

Demons [6] is applied to register the manual labels of prostate, bladder and rectum to refine the pre-alignment. Finally, all the subjects are linearly aligned to a common space. Note that, the well-aligned CT and MR image dataset are only used in image synthesis training step. Learning-Based Multimodal Image Registration 7 2-layer ACM and 10-fold cross validation (leave-2-out) are applied. For SRF, the input patch size is 15*15*15 and the target patch size is 3*3*3. We use 25 trees to synthesize MRI from CT, while 20 trees to synthesize CT from MRI.

Our focus is on the registration of inner lung structures, hence we use lung segmentations as in [16] and thereby avoid sliding issues. Contributions: We focus on the relaxation formulation of LDDMM [2], but the approach could easily be generalized to shooting formulations [1,18]. Our scheme is based on the smoothness of transformations and velocities computed with LDDMM allowing discretizations at lower spatial resolution and consequentially substantially reduced memory requirements. The image match is computed at the original resolution thereby maintaining accurate results.

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